Ankündigung des Promotionsvortrags von: Michael Moser
An important open question in particle physics is if the neutrino mixing matrix is unitary or not.
Currently, the uncertainties on several matrix elements are too large in order to draw significant conclusions.
This is mostly due to the low experimental statistics in the tau neutrino sector.
KM3NeT/ORCA is a water Cherenkov neutrino detector under construction with several megatons of instrumented volume.
It will observe about 4000 tau neutrino events per year, which will significantly improve the available tau neutrino statistics.
For the detection of tau neutrinos, a precise event reconstruction based on the low-level detector data is crucial.
The reconstruction of the detector data consists of several stages.
First, the detector background, consisting of atmospheric muons and 40K decays in seawater, is distinguished from neutrino signals.
Then, track-like events (charged current muon neutrino events), are separated from cascade-like events (charged and neutral current electron neutrino events).
At last, neutrino properties like the energy and the direction of the neutrinos are reconstructed.
A novel technique for these tasks is to employ deep neural networks.
Within this work, convolutional neural networks (CNNs) have been designed for each of the aforementioned tasks.
It is shown that this first application of a CNN-based event reconstruction yields competitive results and performance improvements with respect to classical approaches.
Applying the CNN-based reconstruction to an analysis on the sensitivity of KM3NeT/ORCA to the appearance of tau neutrinos shows that the sensitivity can be improved by more than 10% with respect to the currently employed reconstruction techniques.
(Vortrag auf Deutsch)
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